电子测量与仪器学报

李耀华,杨通江,张宇.基于改进SDAE-GATransformer的航空变压整流器故障诊断方法[J].电子测量与仪器学报,2025,39(11):203-213

基于改进SDAE-GATransformer的航空变压整流器故障诊断方法

Fault diagnosis method of aircraft transformer rectifier unit basedon improved SDAE-GATransformer

DOI：

中文关键词: 故障诊断变压整流器堆叠降噪自动编码器 Transformer

英文关键词:fault diagnosis transformer rectifier unit stacked denoising autoencoders transformer

基金项目:国家自然科学基金委员会-中国民航局民航联合研究基金（U2033209）项目资助

作者	单位
李耀华	中国民航大学交通科学与工程学院天津300300
杨通江	中国民航大学交通科学与工程学院天津300300
张宇	中国民航大学航空工程学院天津300300

Author	Institution
Li Yaohua	College of Transportation Science and Engineering, Civil Aviation University of China, Tianjin 300300, Chin
Yang Tongjiang	College of Transportation Science and Engineering, Civil Aviation University of China, Tianjin 300300, Chin
Zhang Yu	College of Aeronautical Engineering, Civil Aviation University of China, Tianjin 300300, China

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中文摘要:

航空变压整流器（transformer rectifier unit, TRU）是飞机二次电源系统中关键电能变换装置之一，在TRU工作过程中易受温湿度变化和负载波动的影响导致其组成元件出现相应故障，降低设备的可靠性继而影响飞航安全。针对TRU硬件故障类别多且故障数据特征相似导致故障定位困难的问题，提出一种基于改进堆叠降噪自编码器（stacked denoising auto encoder, SDAE）结合遗传算法（genetic algorithm, GA）优化Transformer的故障诊断方法。首先，对采集的故障数据进行归一化处理；其次，在SDAE训练阶段引入对比中心损失（contrastive center loss, CCL）函数，利用样本标签信息在SDAE逐层非线性映射中学习最佳分类特征，实现类内距离缩小，类间距离扩大。同时，将CCL与重构成本损失（reconstructing cost losses, RCL）函数联合优化得到基于改进SDAE特征提取模块，实现对原始故障数据的特征预提取。为进一步提取特征信息并诊断，构建GA优化Transformer的诊断模块，提高故障检测的准确率。最后，利用Simulink仿真故障数据与现有诊断方法进行对比研究。结果表明，所提方法可以较好的实现101种故障的诊断，准确率达96.05%，且具有良好的抗噪能力。

英文摘要:

The transformer rectifier unit (TRU) is one of the key power conversion devices in the secondary power supply system of an airplane. During the operation of the TRU, it is susceptible to temperature and humidity variations and load fluctuations, leading to corresponding failures of its components, which reduces the reliability of the equipment and then affects the safety of flight. In view of the problem that TRU hardware has many fault categories and similar fault data characteristics, a fault diagnosis method based on stacked denoising auto encoder (SDAE) combined with genetic algorithm (GA) to optimize the Transformer is proposed. The following is an example of the optimization of Transformer’s fault diagnosis method. First, the collected fault data are normalized; second, the contrastive center loss (CCL) function is introduced in the training phase of SDAE to learn the optimal classification features in the layer-by-layer nonlinear mapping of SDAE by using the sample label information, so as to realize the reduction of the distance within classes and the expansion of the distance between classes. At the same time, the CCL and reconstructing cost losses (RCL) function are jointly optimized to obtain the improved SDAE-based feature extraction module, which realizes the feature pre-extraction of the original fault data; in order to further extract the feature information and diagnose the problem, the diagnostic module of the GA-optimized Transformer is constructed to improve the accuracy of fault detection. Finally, Simulink is utilized to simulate the fault data to compare with the existing diagnostic methods. The results show that the proposed method can better realize the diagnosis of 101 kinds of faults, with an accuracy rate of 96.05% and good noise resistance.

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